the flink-conf.yaml has set the fs.hdfs.hadoopconf property to the Hadoop configuration directory. For automated testing or running from an IDE the directory containing flink-conf.yaml can be set by defining the FLINK_CONF_DIR environment variable.

the Hadoop configuration (in that directory) has an entry for the required file system in a file core-site.xml. Examples for S3 and Alluxio are linked/shown below.

the required classes for using the file system are available in the lib/ folder of the Flink installation (on all machines running Flink). If putting the files into the directory is not possible, Flink also respects the HADOOP_CLASSPATH environment variable to add Hadoop jar files to the classpath.

Alluxio

Connecting to other systems using Input/OutputFormat wrappers for Hadoop

Apache Flink allows users to access many different systems as data sources or sinks.
The system is designed for very easy extensibility. Similar to Apache Hadoop, Flink has the concept
of so called InputFormats and OutputFormats.

One implementation of these InputFormats is the HadoopInputFormat. This is a wrapper that allows
users to use all existing Hadoop input formats with Flink.

Avro support in Flink

Flink has extensive build-in support for Apache Avro. This allows to easily read from Avro files with Flink.
Also, the serialization framework of Flink is able to handle classes generated from Avro schemas. Be sure to include the Flink Avro dependency to the pom.xml of your project.

Note that User is a POJO generated by Avro. Flink also allows to perform string-based key selection of these POJOs. For example:

usersDS.groupBy("name")

Note that using the GenericData.Record type is possible with Flink, but not recommended. Since the record contains the full schema, its very data intensive and thus probably slow to use.

Flink’s POJO field selection also works with POJOs generated from Avro. However, the usage is only possible if the field types are written correctly to the generated class. If a field is of type Object you can not use the field as a join or grouping key.
Specifying a field in Avro like this {"name": "type_double_test", "type": "double"}, works fine, however specifying it as a UNION-type with only one field ({"name": "type_double_test", "type": ["double"]},) will generate a field of type Object. Note that specifying nullable types ({"name": "type_double_test", "type": ["null", "double"]},) is possible!

Access Microsoft Azure Table Storage

Note: This example works starting from Flink 0.6-incubating

This example is using the HadoopInputFormat wrapper to use an existing Hadoop input format implementation for accessing Azure’s Table Storage.

Download and compile the azure-tables-hadoop project. The input format developed by the project is not yet available in Maven Central, therefore, we have to build the project ourselves.
Execute the following commands:

flink-hadoop-compatibility is a Flink package that provides the Hadoop input format wrappers.
microsoft-hadoop-azure is adding the project we’ve build before to our project.

The project is now prepared for starting to code. We recommend to import the project into an IDE, such as Eclipse or IntelliJ. (Import as a Maven project!).
Browse to the code of the Job.java file. Its an empty skeleton for a Flink job.

Paste the following code into it:

importjava.util.Map;importorg.apache.flink.api.common.functions.MapFunction;importorg.apache.flink.api.java.DataSet;importorg.apache.flink.api.java.ExecutionEnvironment;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.hadoopcompatibility.mapreduce.HadoopInputFormat;importorg.apache.hadoop.io.Text;importorg.apache.hadoop.mapreduce.Job;importcom.microsoft.hadoop.azure.AzureTableConfiguration;importcom.microsoft.hadoop.azure.AzureTableInputFormat;importcom.microsoft.hadoop.azure.WritableEntity;importcom.microsoft.windowsazure.storage.table.EntityProperty;publicclassAzureTableExample{publicstaticvoidmain(String[]args)throwsException{// set up the execution environmentfinalExecutionEnvironmentenv=ExecutionEnvironment.getExecutionEnvironment();// create a AzureTableInputFormat, using a Hadoop input format wrapperHadoopInputFormat<Text,WritableEntity>hdIf=newHadoopInputFormat<Text,WritableEntity>(newAzureTableInputFormat(),Text.class,WritableEntity.class,newJob());// set the Account URI, something like: https://apacheflink.table.core.windows.nethdIf.getConfiguration().set(AzureTableConfiguration.Keys.ACCOUNT_URI.getKey(),"TODO");// set the secret storage key herehdIf.getConfiguration().set(AzureTableConfiguration.Keys.STORAGE_KEY.getKey(),"TODO");// set the table name herehdIf.getConfiguration().set(AzureTableConfiguration.Keys.TABLE_NAME.getKey(),"TODO");DataSet<Tuple2<Text,WritableEntity>>input=env.createInput(hdIf);// a little example how to use the data in a mapper.DataSet<String>fin=input.map(newMapFunction<Tuple2<Text,WritableEntity>,String>(){@OverridepublicStringmap(Tuple2<Text,WritableEntity>arg0)throwsException{System.err.println("--------------------------------\nKey = "+arg0.f0);WritableEntitywe=arg0.f1;for(Map.Entry<String,EntityProperty>prop:we.getProperties().entrySet()){System.err.println("key="+prop.getKey()+" ; value (asString)="+prop.getValue().getValueAsString());}returnarg0.f0.toString();}});// emit result (this works only locally)fin.print();// execute programenv.execute("Azure Example");}}

The example shows how to access an Azure table and turn data into Flink’s DataSet (more specifically, the type of the set is DataSet<Tuple2<Text, WritableEntity>>). With the DataSet, you can apply all known transformations to the DataSet.